SAVRN Model Hub
Open-Weight Models
An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data.
Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page.
Updated 2026-10-18 · How the library is built
11,122 models, sorted by most downloaded.
An ACT (Action Chunking Transformer) imitation-learning policy trained on an SO-ARM101 follower arm equipped with an AmazingHand dexterous hand, performing a cube pick-up task. - 20 teleoperated demonstrations, 18,538 frames, 30 fps - The cube was recorded at 4 different table positions, 5 episodes each, to cover positional variation shoulderpan.pos, shoulderlift.pos, elbowflex.pos, wristflex.pos, wristroll.pos, gripper.pos Requires an environment matching this project (a customized lerobot that includes the soamazinghand robot definition): 1. --task must match the training singletask string exactly 2. Camera names and index order must match training 3. The robot must already be calibrated…
Model checkpoints for the paper Self-Play Pretraining with Zero Data. Two randomly initialized transformers are trained in tandem: a generator proposes programs for a minimal universal Turing machine, and a learner is trained by next-token prediction on the executed byte sequences. No natural data is used at any point during training. These checkpoints are the learners from that process, released so that every result in the paper can be recomputed from the weights. All models are byte-level (vocabulary 256) decoder-only Llama-style transformers with a 4096-token context. The main self-play ladder, six model sizes. Learner weights are saved every 256 self-play rounds; sizes refer to…
weighted/imatrix quants of https://huggingface.co/oolabs/sommerfugl-31b-v1 For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/sommerfugl-31b-v1-GGUF This is a vision model - mmproj files (if any) will be in the static repository. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…
library\name: ultralytics \- object-detection \- yolo \- sonar \- side-scan-sonar \- marine-debris \- underwater \- computer-vision \- pytorch \- onnx \# SONARINTEL — Side-Scan Sonar Object Detection YOLO11M object detection model trained for detecting underwater objects and anomalies in \\Side-Scan Sonar (SSS)\\ imagery. The model is part of the \\SONARINTEL\\ project, an AI-powered system for automated underwater marine debris and anomaly detection using side-scan sonar imagery. \## Model Details \- \\Architecture:\\ YOLO11M \- \\Task:\\ Object Detection \- \\Input Size:\\ 640 × 640 \- \\Number of Classes:\\ 5 \- \\Training Experiment:\\ EXP-01 \- \\Framework:\\ Ultralytics YOLO \…
An int8, ONNX version of facebook/wav2vec2-lv-60-espeak-cv-ft, a wav2vec 2.0 model that outputs espeak IPA phoneme labels (not words). It is the file the speech service of Sonari, an English course for Vietnamese learners, runs on CPU to score pronunciation: CTC forced alignment of the learner's recording against the expected phonemes, then a goodness-of-pronunciation (GOP) score per phoneme. It is not used to transcribe speech. Only the weights' format and precision changed. No training or fine-tuning was done. Use the commit-pinned URL above, not main: Sonari pins this exact file by SHA-256 and refuses any other, because its thresholds are calibrated on it. Later commits to this…
This repository hosts the 035 / 200k weights and a separate Rockchip build sysroot. Built on GR00T-WholeBodyControl / SONIC. Thanks to the GEAR team for their amazing and solid work, comprehensive open-source release and thorough documentation. The 035 step-200,000 weights (PT, ONNX and RKNN) and accompanying release configurations are licensed under the Apache License 2.0. Third-party software in the sysroot retains its own license terms. Real-robot motions can fail or cause falls. Validate in MuJoCo first, retain a safety harness / gantry on hardware, and protect all people nearby. Use the source repository downloader to select PT, ONNX or RKNN artifacts. It also fetches the root…
Runtime assets use neutral filenames. This is an application package; underlying components retain their original licenses and authorship.
This model was finetuned and converted to GGUF format using Unsloth. - Qwen2.5-32B-Instruct.Q4KM.gguf An Ollama Modelfile is included for easy deployment. This was trained 2x faster with Unsloth
span-02 is a decision model: it answers typed questions about a state (JSON or text, plus up to 4 images) with a full probability distribution over the options, in the Decision Index /v1/systemone wire format. It is a fine-tune of Gemma 4 26B-A4B (mixture of experts, about 4B active parameters per token), quantized (NVFP4 experts, FP8 dense layers) and served by a custom Rust/CUDA engine for Blackwell GPUs (sm120: RTX 5090, RTX PRO 6000) that is included in this repo (engine/). Before answering it may think. If the top probability of the no-think answer is below 0.8, the model writes a greedy thought. Its length limit comes from a deterministic thought budget: a fixed cost model…
Synthesise music from MIDI, or add, remove, and modify notes in a recording by revising its MIDI. SpanSynth-Edit generates the selected region, using surrounding audio for timbre guidance. Try it in your browser: upload audio, transcribe with YourMT3+, edit the piano roll, and generate. See the local web setup to run the app yourself. - CPU inference is also supported. Measured peak VRAM was about 3.9 GiB on a GH200 for synthesis and both editing methods with default settings (20.48 s crop, 16 steps, CFG 2.0). Install PyTorch for your GPU first, then install the CLI without downloading the demo audio: Model and codec weights download automatically on first use. No token is required.…
Darwin uniform-soup of 3 Spark2.5-1.7B donors (226/226 tensors verified). Agentic/tool-use + uncensored + multilingual blend. Distillation donor for the 4B program. Needs transformers==5.17.0 + trustremotecode=True.
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Open-Weight Models Explained
What is an open-weight model?
An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls.
Is an open-weight model the same as open source?
Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only.
Can I use an open-weight model commercially?
It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license.
How much memory does an open-weight model need?
About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
SAVRN Index
What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.